The present invention relates, in general, to inspection of semiconductor wafers, and more particularly to a method of processing images of a patterned workpiece to enhance the inspection process.
Defect density is known to be a major yield limit in semiconductor manufacture and must be monitored to provide data for yield control. Accurate defect density measurements can also be used to predict reliability and lifetime of an integrated circuit. Manual inspection is rather simple and requires relatively low cost equipment, but the results are inconsistent because of the subjective nature of the assessment and the attention span of the operator. Further, the time required to process the wafers as well as the limited amount of information that may be readily obtained limits the application of manual inspection techniques to statistical sampling. In practice, this detection procedure is carried out on only a small percentage of the processed wafers. Such a procedure is grossly inefficient in that 90% or more of the processed circuits are never inspected. Further, as the circuits become more complicated and patterns become smaller, it becomes increasingly difficult to see defects let alone classify such defects. Present methods of integrated circuit inspection provide only estimates of defect density and thus can not fulfill the greater needs of the semiconductor industry.
The semiconductor industry has developed a variety of automatic workpiece inspection systems to fill this need. These systems can inspect all the circuits of many wafers in a time efficient manner, but certain types of defect cannot be detected reliably. One of the basic problems associated with such automation is the methods used to separate defects from the desired patterns on the workpiece. In the past this image processing has been directed to detection, classification and comparison of shape and edge information. The prior art includes many methods based on one or both of these image characteristics. These approaches give good results if there are clear edges to the defect, but is less effective with defects which do not have distinct edges. Defects such as breaks in objects or extraneous particles tend to form distinct edges. As a result these defects are well suited to the detection methods of the prior art. There is another class of defects such as stains, smears and some types of surface scratches which tend to blend into other shapes rather than forming distinct edges. Edge or shape detection is not sufficient for this class of defect. In addition, an edge or shape detection method is highly dependent upon the specific lighting conditions used for inspection. Any change of color, light intensity, or angle of illumination will alter the edges detected in some way. The nature of edge detection also requires that the defect span several pixels to form a detectable edge. This requirement limits the minimum size of defect which can be detected by the use of such methods.
There exists a need for an automated workpiece inspection system which can detect the class of defects having no distinct edges. The system must be sensitive to objects which are as small as one or two pixels, but must be insensitive to lighting conditions during the inspection process.